Overview

Dataset statistics

Number of variables22
Number of observations5180
Missing cells1896
Missing cells (%)1.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory890.4 KiB
Average record size in memory176.0 B

Variable types

Numeric10
Categorical12

Alerts

Age is highly correlated with WorkExperienceHigh correlation
WorkExperience is highly correlated with Age and 2 other fieldsHigh correlation
LastPromotion is highly correlated with CurrentProfileHigh correlation
CurrentProfile is highly correlated with WorkExperience and 1 other fieldsHigh correlation
MonthlyIncome is highly correlated with WorkExperienceHigh correlation
Age is highly correlated with WorkExperienceHigh correlation
WorkExperience is highly correlated with Age and 1 other fieldsHigh correlation
LastPromotion is highly correlated with CurrentProfileHigh correlation
CurrentProfile is highly correlated with LastPromotionHigh correlation
MonthlyIncome is highly correlated with WorkExperienceHigh correlation
Age is highly correlated with WorkExperienceHigh correlation
WorkExperience is highly correlated with Age and 1 other fieldsHigh correlation
MonthlyIncome is highly correlated with WorkExperienceHigh correlation
Department is highly correlated with EducationFieldHigh correlation
EducationField is highly correlated with DepartmentHigh correlation
Age is highly correlated with Designation and 1 other fieldsHigh correlation
Department is highly correlated with EducationFieldHigh correlation
EducationField is highly correlated with DepartmentHigh correlation
Designation is highly correlated with Age and 3 other fieldsHigh correlation
WorkExperience is highly correlated with Age and 4 other fieldsHigh correlation
LastPromotion is highly correlated with WorkExperience and 1 other fieldsHigh correlation
CurrentProfile is highly correlated with Designation and 2 other fieldsHigh correlation
MaritalStatus is highly correlated with MonthlyIncomeHigh correlation
MonthlyIncome is highly correlated with Designation and 2 other fieldsHigh correlation
Age has 316 (6.1%) missing values Missing
Department has 124 (2.4%) missing values Missing
HomeToWork has 255 (4.9%) missing values Missing
HourlnWeek has 287 (5.5%) missing values Missing
SalaryHikelastYear has 169 (3.3%) missing values Missing
WorkExperience has 187 (3.6%) missing values Missing
LastPromotion has 70 (1.4%) missing values Missing
CurrentProfile has 311 (6.0%) missing values Missing
MonthlyIncome has 93 (1.8%) missing values Missing
EmployeeID is uniformly distributed Uniform
EmployeeID has unique values Unique
NumCompaniesWorked has 377 (7.3%) zeros Zeros
LastPromotion has 1201 (23.2%) zeros Zeros
CurrentProfile has 624 (12.0%) zeros Zeros

Reproduction

Analysis started2022-06-17 19:22:00.916486
Analysis finished2022-06-17 19:22:35.193399
Duration34.28 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

EmployeeID
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct5180
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5112590.5
Minimum5110001
Maximum5115180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.6 KiB
2022-06-18T00:52:35.373399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum5110001
5-th percentile5110259.95
Q15111295.75
median5112590.5
Q35113885.25
95-th percentile5114921.05
Maximum5115180
Range5179
Interquartile range (IQR)2589.5

Descriptive statistics

Standard deviation1495.481528
Coefficient of variation (CV)0.0002925095463
Kurtosis-1.2
Mean5112590.5
Median Absolute Deviation (MAD)1295
Skewness0
Sum2.648321879 × 1010
Variance2236465
MonotonicityStrictly increasing
2022-06-18T00:52:35.577438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51141981
 
< 0.1%
51117771
 
< 0.1%
51121301
 
< 0.1%
51147901
 
< 0.1%
51127311
 
< 0.1%
51133131
 
< 0.1%
51106501
 
< 0.1%
51107621
 
< 0.1%
51149491
 
< 0.1%
51137701
 
< 0.1%
Other values (5170)5170
99.8%
ValueCountFrequency (%)
51100011
< 0.1%
51100021
< 0.1%
51100031
< 0.1%
51100041
< 0.1%
51100051
< 0.1%
51100061
< 0.1%
51100071
< 0.1%
51100081
< 0.1%
51100091
< 0.1%
51100101
< 0.1%
ValueCountFrequency (%)
51151801
< 0.1%
51151791
< 0.1%
51151781
< 0.1%
51151771
< 0.1%
51151761
< 0.1%
51151751
< 0.1%
51151741
< 0.1%
51151731
< 0.1%
51151721
< 0.1%
51151711
< 0.1%

Attrition
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size40.6 KiB
0.0
3735 
1.0
1445 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03735
72.1%
1.01445
 
27.9%

Length

2022-06-18T00:52:35.761402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-18T00:52:35.862824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.03735
72.1%
1.01445
 
27.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct44
Distinct (%)0.9%
Missing316
Missing (%)6.1%
Infinite0
Infinite (%)0.0%
Mean37.10855263
Minimum18
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.6 KiB
2022-06-18T00:52:35.985788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile23
Q130
median36
Q343
95-th percentile55
Maximum61
Range43
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.248646568
Coefficient of variation (CV)0.2492322096
Kurtosis-0.3890514417
Mean37.10855263
Median Absolute Deviation (MAD)6
Skewness0.4264486746
Sum180496
Variance85.53746334
MonotonicityNot monotonic
2022-06-18T00:52:36.156138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
35253
 
4.9%
36236
 
4.6%
32231
 
4.5%
33221
 
4.3%
31217
 
4.2%
34216
 
4.2%
29204
 
3.9%
30203
 
3.9%
37196
 
3.8%
38181
 
3.5%
Other values (34)2706
52.2%
(Missing)316
 
6.1%
ValueCountFrequency (%)
1822
 
0.4%
1924
 
0.5%
2045
 
0.9%
2155
 
1.1%
2255
 
1.1%
2350
 
1.0%
2460
1.2%
2599
1.9%
26117
2.3%
27138
2.7%
ValueCountFrequency (%)
617
 
0.1%
6025
 
0.5%
5937
0.7%
5835
0.7%
5732
0.6%
5652
1.0%
5568
1.3%
5452
1.0%
5357
1.1%
5269
1.3%

TravelProfile
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size40.6 KiB
Rarely
3637 
Yes
1051 
No
492 

Length

Max length6
Median length6
Mean length5.011389961
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRarely
2nd rowYes
3rd rowRarely
4th rowYes
5th rowNo

Common Values

ValueCountFrequency (%)
Rarely3637
70.2%
Yes1051
 
20.3%
No492
 
9.5%

Length

2022-06-18T00:52:36.340138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-18T00:52:36.463105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
rarely3637
70.2%
yes1051
 
20.3%
no492
 
9.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Department
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.1%
Missing124
Missing (%)2.4%
Memory size40.6 KiB
Analytics
3219 
Sales
1615 
Marketing
 
222

Length

Max length9
Median length9
Mean length7.722310127
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAnalytics
2nd rowSales
3rd rowAnalytics
4th rowSales
5th rowAnalytics

Common Values

ValueCountFrequency (%)
Analytics3219
62.1%
Sales1615
31.2%
Marketing222
 
4.3%
(Missing)124
 
2.4%

Length

2022-06-18T00:52:36.606138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-18T00:52:36.734145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
analytics3219
63.7%
sales1615
31.9%
marketing222
 
4.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HomeToWork
Real number (ℝ≥0)

MISSING

Distinct35
Distinct (%)0.7%
Missing255
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean11.10741117
Minimum1
Maximum121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.6 KiB
2022-06-18T00:52:36.861138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median9
Q316
95-th percentile28
Maximum121
Range120
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.455577275
Coefficient of variation (CV)0.761255449
Kurtosis5.32968112
Mean11.10741117
Median Absolute Deviation (MAD)5
Skewness1.269843274
Sum54704
Variance71.49678706
MonotonicityNot monotonic
2022-06-18T00:52:37.039609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
3593
 
11.4%
5444
 
8.6%
1320
 
6.2%
11270
 
5.2%
10260
 
5.0%
6256
 
4.9%
9245
 
4.7%
8240
 
4.6%
4225
 
4.3%
7206
 
4.0%
Other values (25)1866
36.0%
(Missing)255
 
4.9%
ValueCountFrequency (%)
1320
6.2%
266
 
1.3%
3593
11.4%
4225
 
4.3%
5444
8.6%
6256
4.9%
7206
 
4.0%
8240
4.6%
9245
4.7%
10260
5.0%
ValueCountFrequency (%)
1211
 
< 0.1%
362
 
< 0.1%
342
 
< 0.1%
3239
 
0.8%
3132
 
0.6%
3072
1.4%
2977
1.5%
2867
1.3%
2799
1.9%
2671
1.4%

EducationField
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size40.6 KiB
Statistics
2129 
CA
1560 
Marketing Diploma
603 
Engineer
487 
Other
284 

Length

Max length17
Median length10
Mean length7.785328185
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCA
2nd rowStatistics
3rd rowStatistics
4th rowStatistics
5th rowStatistics

Common Values

ValueCountFrequency (%)
Statistics2129
41.1%
CA1560
30.1%
Marketing Diploma603
 
11.6%
Engineer487
 
9.4%
Other284
 
5.5%
MBA117
 
2.3%

Length

2022-06-18T00:52:37.234602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-18T00:52:37.358565image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
statistics2129
36.8%
ca1560
27.0%
marketing603
 
10.4%
diploma603
 
10.4%
engineer487
 
8.4%
other284
 
4.9%
mba117
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Gender
Categorical

Distinct3
Distinct (%)0.1%
Missing46
Missing (%)0.9%
Memory size40.6 KiB
Male
3094 
Female
1338 
F
702 

Length

Max length6
Median length4
Mean length4.111024542
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowF
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male3094
59.7%
Female1338
25.8%
F702
 
13.6%
(Missing)46
 
0.9%

Length

2022-06-18T00:52:37.534616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-18T00:52:37.637605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
male3094
60.3%
female1338
26.1%
f702
 
13.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HourlnWeek
Real number (ℝ≥0)

MISSING

Distinct58
Distinct (%)1.2%
Missing287
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean57.97976701
Minimum10
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.6 KiB
2022-06-18T00:52:37.788676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile35
Q149
median59
Q367
95-th percentile79
Maximum99
Range89
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.99667387
Coefficient of variation (CV)0.2241587805
Kurtosis-0.6327511515
Mean57.97976701
Median Absolute Deviation (MAD)9
Skewness-0.2150486479
Sum283695
Variance168.9135317
MonotonicityNot monotonic
2022-06-18T00:52:37.984642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66178
 
3.4%
54178
 
3.4%
56170
 
3.3%
59165
 
3.2%
68157
 
3.0%
57153
 
3.0%
67151
 
2.9%
58143
 
2.8%
62137
 
2.6%
64134
 
2.6%
Other values (48)3327
64.2%
(Missing)287
 
5.5%
ValueCountFrequency (%)
101
 
< 0.1%
121
 
< 0.1%
3033
0.6%
3125
 
0.5%
3263
1.2%
3351
1.0%
3468
1.3%
3564
1.2%
3650
1.0%
3762
1.2%
ValueCountFrequency (%)
991
 
< 0.1%
891
 
< 0.1%
871
 
< 0.1%
8257
1.1%
8145
0.9%
8099
1.9%
7988
1.7%
7870
1.4%
7765
1.3%
7659
1.1%

Involvement
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size40.6 KiB
3.0
3030 
4.0
1355 
1.0
361 
5.0
325 
2.0
 
109

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row4.0
3rd row5.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.03030
58.5%
4.01355
26.2%
1.0361
 
7.0%
5.0325
 
6.3%
2.0109
 
2.1%

Length

2022-06-18T00:52:38.165638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-18T00:52:38.264640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
3.03030
58.5%
4.01355
26.2%
1.0361
 
7.0%
5.0325
 
6.3%
2.0109
 
2.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

WorkLifeBalance
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size40.6 KiB
4.0
1060 
3.0
1054 
5.0
1033 
1.0
1027 
2.0
1006 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row3.0
4th row2.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.01060
20.5%
3.01054
20.3%
5.01033
19.9%
1.01027
19.8%
2.01006
19.4%

Length

2022-06-18T00:52:38.415825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-18T00:52:38.516826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
4.01060
20.5%
3.01054
20.3%
5.01033
19.9%
1.01027
19.8%
2.01006
19.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Designation
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing38
Missing (%)0.7%
Memory size40.6 KiB
Executive
2072 
Manager
1756 
Senior Manager
763 
AVP
328 
VP
223 

Length

Max length14
Median length9
Mean length8.372617658
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowExecutive
2nd rowExecutive
3rd rowManager
4th rowManager
5th rowManager

Common Values

ValueCountFrequency (%)
Executive2072
40.0%
Manager1756
33.9%
Senior Manager763
 
14.7%
AVP328
 
6.3%
VP223
 
4.3%
(Missing)38
 
0.7%

Length

2022-06-18T00:52:38.686617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-18T00:52:38.807611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
manager2519
42.7%
executive2072
35.1%
senior763
 
12.9%
avp328
 
5.6%
vp223
 
3.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

JobSatisfaction
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size40.6 KiB
3.0
1560 
5.0
1095 
4.0
965 
1.0
847 
2.0
713 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row2.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.01560
30.1%
5.01095
21.1%
4.0965
18.6%
1.0847
16.4%
2.0713
13.8%

Length

2022-06-18T00:52:38.962149image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-18T00:52:39.062149image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
3.01560
30.1%
5.01095
21.1%
4.0965
18.6%
1.0847
16.4%
2.0713
13.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ESOPs
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size40.6 KiB
0.0
2639 
1.0
2541 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.02639
50.9%
1.02541
49.1%

Length

2022-06-18T00:52:39.211153image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-18T00:52:39.307153image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.02639
50.9%
1.02541
49.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NumCompaniesWorked
Real number (ℝ≥0)

ZEROS

Distinct14
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.157335907
Minimum0
Maximum21
Zeros377
Zeros (%)7.3%
Negative0
Negative (%)0.0%
Memory size40.6 KiB
2022-06-18T00:52:39.406157image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35
95-th percentile9
Maximum21
Range21
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.606035564
Coefficient of variation (CV)0.8253906587
Kurtosis1.23453707
Mean3.157335907
Median Absolute Deviation (MAD)1
Skewness1.126676819
Sum16355
Variance6.791421362
MonotonicityNot monotonic
2022-06-18T00:52:39.539155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
11370
26.4%
21092
21.1%
4503
 
9.7%
3495
 
9.6%
0377
 
7.3%
5352
 
6.8%
7280
 
5.4%
6253
 
4.9%
8190
 
3.7%
9187
 
3.6%
Other values (4)81
 
1.6%
ValueCountFrequency (%)
0377
 
7.3%
11370
26.4%
21092
21.1%
3495
 
9.6%
4503
 
9.7%
5352
 
6.8%
6253
 
4.9%
7280
 
5.4%
8190
 
3.7%
9187
 
3.6%
ValueCountFrequency (%)
211
 
< 0.1%
192
 
< 0.1%
183
 
0.1%
1075
 
1.4%
9187
 
3.6%
8190
 
3.7%
7280
5.4%
6253
4.9%
5352
6.8%
4503
9.7%

OverTime
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size40.6 KiB
0.0
3556 
1.0
1624 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03556
68.6%
1.01624
31.4%

Length

2022-06-18T00:52:39.691154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-18T00:52:39.789155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.03556
68.6%
1.01624
31.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SalaryHikelastYear
Real number (ℝ≥0)

MISSING

Distinct16
Distinct (%)0.3%
Missing169
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean20.64937138
Minimum16
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.6 KiB
2022-06-18T00:52:39.884314image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile16
Q118
median20
Q323
95-th percentile28
Maximum31
Range15
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.706469112
Coefficient of variation (CV)0.1794954937
Kurtosis-0.3511777323
Mean20.64937138
Median Absolute Deviation (MAD)3
Skewness0.7789437595
Sum103474
Variance13.73791328
MonotonicityNot monotonic
2022-06-18T00:52:40.022916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
17709
13.7%
19680
13.1%
18668
12.9%
20504
9.7%
16419
8.1%
23299
5.8%
21288
5.6%
24287
5.5%
22275
 
5.3%
25205
 
4.0%
Other values (6)677
13.1%
ValueCountFrequency (%)
16419
8.1%
17709
13.7%
18668
12.9%
19680
13.1%
20504
9.7%
21288
5.6%
22275
 
5.3%
23299
5.8%
24287
5.5%
25205
 
4.0%
ValueCountFrequency (%)
3122
 
0.4%
3057
 
1.1%
29100
 
1.9%
28136
2.6%
27193
3.7%
26169
3.3%
25205
4.0%
24287
5.5%
23299
5.8%
22275
5.3%

WorkExperience
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct42
Distinct (%)0.8%
Missing187
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean11.16583217
Minimum0
Maximum41
Zeros29
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size40.6 KiB
2022-06-18T00:52:40.205916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median9
Q315
95-th percentile27
Maximum41
Range41
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.797783459
Coefficient of variation (CV)0.6983611561
Kurtosis1.05185581
Mean11.16583217
Median Absolute Deviation (MAD)4
Skewness1.148334256
Sum55751
Variance60.80542688
MonotonicityNot monotonic
2022-06-18T00:52:40.370916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
10481
 
9.3%
6397
 
7.7%
7358
 
6.9%
8328
 
6.3%
11321
 
6.2%
9310
 
6.0%
5284
 
5.5%
1229
 
4.4%
2217
 
4.2%
4201
 
3.9%
Other values (32)1867
36.0%
(Missing)187
 
3.6%
ValueCountFrequency (%)
029
 
0.6%
1229
4.4%
2217
4.2%
3157
 
3.0%
4201
3.9%
5284
5.5%
6397
7.7%
7358
6.9%
8328
6.3%
9310
6.0%
ValueCountFrequency (%)
414
 
0.1%
407
 
0.1%
392
 
< 0.1%
388
 
0.2%
3715
0.3%
3610
 
0.2%
3510
 
0.2%
3416
0.3%
3326
0.5%
3223
0.4%

LastPromotion
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct17
Distinct (%)0.3%
Missing70
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean2.552837573
Minimum0
Maximum16
Zeros1201
Zeros (%)23.2%
Negative0
Negative (%)0.0%
Memory size40.6 KiB
2022-06-18T00:52:40.529948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q33
95-th percentile10
Maximum16
Range16
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.234467494
Coefficient of variation (CV)1.267008731
Kurtosis3.732381853
Mean2.552837573
Median Absolute Deviation (MAD)1
Skewness1.973214096
Sum13045
Variance10.46177997
MonotonicityNot monotonic
2022-06-18T00:52:40.678136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
11581
30.5%
01201
23.2%
2838
16.2%
3348
 
6.7%
7208
 
4.0%
4180
 
3.5%
5167
 
3.2%
8133
 
2.6%
6130
 
2.5%
1163
 
1.2%
Other values (7)261
 
5.0%
(Missing)70
 
1.4%
ValueCountFrequency (%)
01201
23.2%
11581
30.5%
2838
16.2%
3348
 
6.7%
4180
 
3.5%
5167
 
3.2%
6130
 
2.5%
7208
 
4.0%
8133
 
2.6%
953
 
1.0%
ValueCountFrequency (%)
1624
 
0.5%
1539
 
0.8%
1427
 
0.5%
1332
 
0.6%
1248
 
0.9%
1163
 
1.2%
1038
 
0.7%
953
 
1.0%
8133
2.6%
7208
4.0%

CurrentProfile
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct19
Distinct (%)0.4%
Missing311
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean4.385294722
Minimum0
Maximum18
Zeros624
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size40.6 KiB
2022-06-18T00:52:41.051097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.617643333
Coefficient of variation (CV)0.8249487349
Kurtosis0.04485311733
Mean4.385294722
Median Absolute Deviation (MAD)2
Skewness0.8021143438
Sum21352
Variance13.08734328
MonotonicityNot monotonic
2022-06-18T00:52:41.195636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3729
14.1%
2721
13.9%
0624
12.0%
1544
10.5%
8490
9.5%
7427
8.2%
4373
7.2%
9257
 
5.0%
5190
 
3.7%
10146
 
2.8%
Other values (9)368
7.1%
(Missing)311
6.0%
ValueCountFrequency (%)
0624
12.0%
1544
10.5%
2721
13.9%
3729
14.1%
4373
7.2%
5190
 
3.7%
697
 
1.9%
7427
8.2%
8490
9.5%
9257
 
5.0%
ValueCountFrequency (%)
187
 
0.1%
1712
 
0.2%
1611
 
0.2%
1514
 
0.3%
1433
 
0.6%
1349
 
0.9%
1263
 
1.2%
1182
 
1.6%
10146
2.8%
9257
5.0%

MaritalStatus
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size40.6 KiB
Single
1776 
Married
1614 
Divorsed
1016 
M
774 

Length

Max length8
Median length7
Mean length5.956756757
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowSingle
3rd rowSingle
4th rowDivorsed
5th rowDivorsed

Common Values

ValueCountFrequency (%)
Single1776
34.3%
Married1614
31.2%
Divorsed1016
19.6%
M774
14.9%

Length

2022-06-18T00:52:41.381680image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-18T00:52:41.498226image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
single1776
34.3%
married1614
31.2%
divorsed1016
19.6%
m774
14.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MonthlyIncome
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2372
Distinct (%)46.6%
Missing93
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean21692.29644
Minimum1000
Maximum95000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.6 KiB
2022-06-18T00:52:41.651226image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile17180
Q118362
median20160
Q323443.5
95-th percentile33065.9
Maximum95000
Range94000
Interquartile range (IQR)5081.5

Descriptive statistics

Standard deviation4770.637922
Coefficient of variation (CV)0.2199231389
Kurtosis11.86381803
Mean21692.29644
Median Absolute Deviation (MAD)2102
Skewness1.996463403
Sum110348712
Variance22758986.19
MonotonicityNot monotonic
2022-06-18T00:52:41.845033image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1740412
 
0.2%
1738011
 
0.2%
187439
 
0.2%
177418
 
0.2%
256098
 
0.2%
179738
 
0.2%
203468
 
0.2%
213478
 
0.2%
189048
 
0.2%
176577
 
0.1%
Other values (2362)5000
96.5%
(Missing)93
 
1.8%
ValueCountFrequency (%)
10001
 
< 0.1%
160096
0.1%
160513
0.1%
160522
 
< 0.1%
160813
0.1%
160913
0.1%
161026
0.1%
161183
0.1%
161292
 
< 0.1%
162002
 
< 0.1%
ValueCountFrequency (%)
950001
 
< 0.1%
359992
< 0.1%
359732
< 0.1%
359431
 
< 0.1%
359261
 
< 0.1%
358594
0.1%
358471
 
< 0.1%
358452
< 0.1%
357401
 
< 0.1%
357171
 
< 0.1%

Interactions

2022-06-18T00:52:31.200077image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:13.911441image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:16.002791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:17.796550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:19.709960image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:21.497866image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:23.480448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:25.432813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:27.212574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:29.230772image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:31.383071image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:14.154385image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:16.189830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:17.995540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:19.895818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:21.680864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:23.682160image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:25.613983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:27.410574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:29.429369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:31.549111image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:14.333379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:16.354794image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:18.174536image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:20.063857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:21.853828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:23.864193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:25.781941image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:27.600531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:29.612369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:31.731072image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:14.534992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:16.542831image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:18.371576image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:20.249818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:22.038861image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:24.055159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:25.965971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:27.808573image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:29.808528image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:31.910936image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:14.720031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:16.723830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:18.552574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:20.421827image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:22.206823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:24.241193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:26.133763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:28.004716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:30.003745image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-18T00:52:26.297798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-18T00:52:31.014382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-18T00:52:42.400004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-18T00:52:42.773828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-18T00:52:43.137853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-06-18T00:52:43.449888image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-18T00:52:33.351153image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-18T00:52:34.091392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-06-18T00:52:34.609758image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-06-18T00:52:34.947399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

EmployeeIDAttritionAgeTravelProfileDepartmentHomeToWorkEducationFieldGenderHourlnWeekInvolvementWorkLifeBalanceDesignationJobSatisfactionESOPsNumCompaniesWorkedOverTimeSalaryHikelastYearWorkExperienceLastPromotionCurrentProfileMaritalStatusMonthlyIncome
05110001.00.035.0RarelyAnalytics5.0CAMale69.01.01.0Executive1.01.01.01.020.07.02.0NaNM18932.0
15110002.01.032.0YesSales5.0StatisticsFemale62.04.03.0Executive2.00.08.00.020.04.01.0NaNSingle18785.0
25110003.00.031.0RarelyAnalytics5.0StatisticsF45.05.03.0Manager2.01.03.00.026.012.01.03.0Single22091.0
35110004.00.034.0YesSales10.0StatisticsFemale32.03.02.0Manager4.01.01.00.023.05.01.03.0Divorsed20302.0
45110005.00.037.0NoAnalytics27.0StatisticsFemale49.03.04.0Manager4.01.08.00.021.012.01.09.0Divorsed21674.0
55110006.01.034.0YesMarketing24.0MBAF43.03.04.0Senior Manager5.01.09.01.020.011.00.02.0Married24950.0
65110007.00.035.0YesSales1.0Marketing DiplomaF62.03.01.0Senior Manager3.01.01.00.019.010.00.08.0Single23789.0
75110008.01.026.0RarelySales8.0Marketing DiplomaMale48.04.02.0Manager3.01.02.01.017.07.01.04.0Single21744.0
85110009.00.045.0YesAnalytics5.0CAMale58.03.03.0Senior Manager3.00.06.01.024.017.03.03.0Single26209.0
95110010.01.024.0RarelySales4.0StatisticsFemale65.03.03.0Manager3.01.09.00.019.04.02.00.0Single19577.0

Last rows

EmployeeIDAttritionAgeTravelProfileDepartmentHomeToWorkEducationFieldGenderHourlnWeekInvolvementWorkLifeBalanceDesignationJobSatisfactionESOPsNumCompaniesWorkedOverTimeSalaryHikelastYearWorkExperienceLastPromotionCurrentProfileMaritalStatusMonthlyIncome
51705115171.00.034.0RarelySales13.0Marketing DiplomaMale54.01.02.0Senior Manager3.01.01.01.020.011.01.09.0Married24380.0
51715115172.01.0NaNYesAnalytics26.0StatisticsFemale66.03.03.0Executive4.01.01.01.024.010.07.08.0M17022.0
51725115173.00.040.0RarelyAnalytics21.0StatisticsMale34.03.04.0Manager3.00.01.00.017.010.02.08.0Single20534.0
51735115174.00.047.0RarelySales5.0Marketing DiplomaF71.03.03.0AVP5.01.09.00.029.029.016.010.0Divorsed33048.0
51745115175.00.0NaNRarelyAnalytics20.0CAMaleNaN3.04.0Executive1.00.01.00.029.02.02.02.0M17323.0
51755115176.00.036.0RarelyAnalytics13.0CAF53.03.03.0Manager3.00.04.01.022.011.01.05.0Single22142.0
51765115177.00.0NaNRarelyMarketing9.0CAFemale66.01.04.0Executive4.01.09.00.023.08.00.02.0Single17109.0
51775115178.00.029.0RarelyAnalyticsNaNCAFemale62.04.02.0Executive1.01.06.00.019.08.00.03.0M17532.0
51785115179.00.026.0RarelyMarketing26.0StatisticsFemale61.03.04.0Executive3.00.01.00.028.08.05.07.0Divorsed17942.0
51795115180.01.0NaNYesSales13.0StatisticsMale74.04.03.0Executive5.01.01.00.023.01.00.00.0Divorsed17033.0